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Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping

Overview of attention for article published in BMC Genomics, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
1 tweeter

Citations

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12 Dimensions

Readers on

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28 Mendeley
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Title
Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping
Published in
BMC Genomics, November 2015
DOI 10.1186/1471-2164-16-s11-s3
Pubmed ID
Authors

Segun Jung, Yingtao Bi, Ramana V Davuluri

Abstract

Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of molecular subgroups by integrating data across different gene expression platforms.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Denmark 1 4%
Italy 1 4%
Canada 1 4%
Unknown 24 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 29%
Student > Ph. D. Student 5 18%
Student > Bachelor 3 11%
Professor > Associate Professor 2 7%
Student > Master 2 7%
Other 3 11%
Unknown 5 18%
Readers by discipline Count As %
Medicine and Dentistry 6 21%
Agricultural and Biological Sciences 4 14%
Computer Science 4 14%
Biochemistry, Genetics and Molecular Biology 3 11%
Mathematics 1 4%
Other 3 11%
Unknown 7 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 November 2015.
All research outputs
#868,680
of 6,815,308 outputs
Outputs from BMC Genomics
#655
of 5,243 outputs
Outputs of similar age
#41,109
of 238,229 outputs
Outputs of similar age from BMC Genomics
#40
of 407 outputs
Altmetric has tracked 6,815,308 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,243 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 238,229 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 407 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.